In today’s oil and gas industry, even though sadly the data management is not still considered by most of oil and gas companies as a serious business need and service required to preserve their assets, knowledge, and efficiency through processes and standards which eventually will help them in increasing their return on investment. Some people argue that it is the responsibility of data management community in the oil and gas industry to step up and show the values of data management to the asset teams instead of expecting them to understand and listen to professional debates about its values.

This article is trying to show some of the key things to understand in building a solid and fruitful data management practice in oil and gas companies.

Data Management in Oil and Gas requires and delivers deep domain skills and expertise to the data that is required by the users and business units.

Challenges come from a variety of changes, restructuring from within the business and from the user community, not the least of which is the evolving position of the industry.

PDM must embrace strategy building, communications and a great deal of adaptability to work with the users and not simply defend the status-quo within an organization.

Delivering good quality data management services through the local data management team and users that you engage with, have trained and mentored.

The data management leadership team needs to be adaptable and able to deal with most of the challenges, through tireless preparation, strategy building, and communications and demonstrate their adaptability to work with the users and business units and not simply defend the status quo.

There needs to be constant review and measurement of data management service delivery as a whole across the user community and business units, designed to resolve issues and offer proactive advice on improvements.

Data management is not a silo; it is there to deliver to the needs of the user community and business units. PDM can never afford to be complacent, can always improve performance and willingness to be proactive and deliver thought leadership.

As custodians of the E&P data we should accept the responsibility for establishing good data management practices and pro-actively address any area which fails to meet those high standards.

Our technical projects teams need to work with IT, the users and business units to establish significant progress and forward planning with respect to the architectural framework, structure and organization for a data management environment that is capable of providing the business with a sustainable data management environment

Much of this work is “original” thinking, “original” concepts born from experience, deep domain knowledge and not a little talent in using the combined intellect to fashion solutions fit for the market and industry we serve. This provides users and the business units with a solid foundation upon which they can expand and utilize data to the singular advantage of the business.

Challenges and frustrations with respect to budgets and project implementation are an ever present drag on progress; the challenge is for data management professionals to “sell” the added value advantages to the business of good quality data management.

To deliver on the above, we need to remain focused, aware, and flexible and above all maintain good communications across the industry, industry strategy, evolution and developments. Industry engagement at all levels underpins the competence of professional data managers, being engaged and involved ensures the latest techniques, standards and thinking is being introduced into each business unit.

Data management is underpinned when CIP, Continuous Improvement Programmes have been established to monitor and manage the improvements in data availability and data quality and to continuously present the benefits back to the users. The combination of data quality, availability and feedback promotes data management from within.

Key data management skills are to leverage tools and skills, to address pockets of poor quality data on a proactive basis, feeding back the results and progress to the user community.